Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer

Prediction of disease severity using serum biomarkers in patients with mild-moderate Atopic Dermatitis: A pilot study

Atopic dermatitis (AD) is an inflammatory skin condition that relies largely on subjective evaluation of clinical signs and symptoms for diagnosis and severity assessment. Using multivariate data, we attempted to construct prediction models that can diagnose the disease and assess its severity. We combined data from 28 mild-moderate AD patients and 20 healthy controls (HC) to create random forest models for classification (AD vs. HC) and regression analysis to predict symptom severities. The classification model outperformed the random permutation model significantly (area under the curve: 0.85 ± 0.10 vs. 0.50 ± 0.15; balanced accuracy: 0.81 ± 0.15 vs. 0.50 ± 0.15). Correlation analysis revealed a significant positive correlation between measured and predicted total SCORing Atopic Dermatitis score (SCORAD; r = 0.43), objective SCORAD (r = 0.53), eczema area and severity index scores (r = 0.58, each p < 0.001), but not between measured and predicted itch ratings (r = 0.21, p = 0.18). We developed and tested multivariate prediction models and identified important features using a variety of serum biomarkers, implying that discovering the deep-branching relationships between clinical measurements and serum measurements in mild-moderate AD patients may be possible using a multivariate machine learning method. We also suggest future methods for utilizing machine learning algorithms to enhance drug target selection, diagnosis, prognosis, and customized treatment in AD.

Blood-Based Biomarkers for Managing Workload in Athletes: Perspectives for Research on Emerging Biomarkers

At present, various blood-based biomarkers have found their applications in the field of sports medicine. This current opinion addresses biomarkers that warrant consideration in future research for monitoring the athlete training load. In this regard, we identified a variety of emerging load-sensitive biomarkers, e.g., cytokines (such as IL-6), chaperones (such as heat shock proteins) or enzymes (such as myeloperoxidase) that could improve future athlete load monitoring as they have shown meaningful increases in acute and chronic exercise settings. In some cases, they have even been linked to training status or performance characteristics. However, many of these markers have not been extensively studied and the cost and effort of measuring these parameters are still high, making them inconvenient for practitioners so far. We therefore outline strategies to improve knowledge of acute and chronic biomarker responses, including ideas for standardized study settings. In addition, we emphasize the need for methodological advances such as the development of minimally invasive point-of-care devices as well as statistical aspects related to the evaluation of these monitoring tools to make biomarkers suitable for regular load monitoring.

The Biomarker Ki-67: Promise, Potential, and Problems in Breast Cancer

Ki-67 is a nuclear protein serendipitously discovered by monoclonal antibody selection in the early 1980s. While it has been applied for decades in the context of breast cancer as a putative prognostic and, more recently, predictive, biomarker, even after all this time there is incomplete agreement as to the validity of the immunohistochemical assays employed for Ki-67 assessment, given possible effects of the disparate methodologies employed and possible confounding preanalytical, analytical, and interpretive variables. In this brief review, the history of Ki-67 and the problems, particularly with the analytical and interpretive variables, are highlighted through a selective review of the published literature. The contributions of the International Ki-67 Breast Cancer Working Group are highlighted, and in particular, the recommendations made by this group are reviewed. The potential of Ki-67 as a biomarker for breast cancer has not yet been fully realized, but an understanding of the power as well as the limitations of the methods of Ki-67 assessment are important if this biomarker can realize its potential.

Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology

Breast cancer remains a highly prevalent disease with considerable inter- and intra-tumoral heterogeneity complicating prognostication and treatment decisions. The utilization and depth of genomic, transcriptomic and proteomic data for cancer has exploded over recent times and the addition of spatial context to this information, by understanding the correlating morphologic and spatial patterns of cells in tissue samples, has created an exciting frontier of research, histo-genomics. At the same time, deep learning (DL), a class of machine learning algorithms employing artificial neural networks, has rapidly progressed in the last decade with a confluence of technical developments - including the advent of modern graphic processing units (GPU), allowing efficient implementation of increasingly complex architectures at scale; advances in the theoretical and practical design of network architectures; and access to larger datasets for training - all leading to sweeping advances in image classification and object detection. In this review, we examine recent developments in the application of DL in breast cancer histology with particular emphasis of those producing biologic insights or novel biomarkers, spanning the extraction of genomic information to the use of stroma to predict cancer recurrence, with the aim of suggesting avenues for further advancing this exciting field.

The Approach of Artificial Intelligence in Neuroendocrine Carcinomas of the Breast: A Next Step towards Precision Pathology?—A Case Report and Review of the Literature

Primary neuroendocrine tumors (NETs) of the breast are considered a rare and undervalued subtype of breast carcinoma that occur mainly in postmenopausal women and are graded as G1 or G2 NETs or an invasive neuroendocrine carcinoma (NEC) (small cell or large cell). To establish a final diagnosis of breast carcinoma with neuroendocrine differentiation, it is essential to perform an immunohistochemical profile of the tumor, using antibodies against synaptophysin or chromogranin, as well as the MIB-1 proliferation index, one of the most controversial markers in breast pathology regarding its methodology in current clinical practice. A standardization error between institutions and pathologists regarding the evaluation of the MIB-1 proliferation index is present. Another challenge refers to the counting process of MIB-1′s expressiveness, which is known as a time-consuming process. The involvement of AI (artificial intelligence) automated systems could be a solution for diagnosing early stages, as well. We present the case of a post-menopausal 79-year-old woman diagnosed with primary neuroendocrine carcinoma of the breast (NECB). The purpose of this paper is to expose the interpretation of MIB-1 expression in our patient’ s case of breast neuroendocrine carcinoma, assisted by artificial intelligence (AI) software (HALO—IndicaLabs), and to analyze the associations between MIB-1 and common histopathological parameters.

Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers

Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains of oncology. However, the potential applications of AI and the barriers to its widespread adoption remain unclear. This study aimed to address this gap by conducting a cross-sectional, global, web-based survey of over 1000 AI and cancer researchers. The results indicated that most respondents believed AI would positively impact cancer grading and classification, follow-up services, and diagnostic accuracy. Despite these benefits, several limitations were identified, including difficulties incorporating AI into clinical practice and the lack of standardization in cancer health data. These limitations pose significant challenges, particularly regarding testing, validation, certification, and auditing AI algorithms and systems. The results of this study provide valuable insights for informed decision-making for stakeholders involved in AI and cancer research and development, including individual researchers and research funding agencies.

Artificial Intelligence and Cancer Control: Toward Prioritizing Justice, Equity, Diversity, and Inclusion (JEDI) in Emerging Decision Support Technologies

PURPOSE FOR REVIEW

This perspective piece has two goals: first, to describe issues related to artificial intelligence-based applications for cancer control as they may impact health inequities or disparities; and second, to report on a review of systematic reviews and meta-analyses of artificial intelligence-based tools for cancer control to ascertain the extent to which discussions of justice, equity, diversity, inclusion, or health disparities manifest in syntheses of the field's best evidence.

RECENT FINDINGS

We found that, while a significant proportion of existing syntheses of research on AI-based tools in cancer control use formal bias assessment tools, the fairness or equitability of models is not yet systematically analyzable across studies. Issues related to real-world use of AI-based tools for cancer control, such as workflow considerations, measures of usability and acceptance, or tool architecture, are more visible in the literature, but still addressed only in a minority of reviews. Artificial intelligence is poised to bring significant benefits to a wide range of applications in cancer control, but more thorough and standardized evaluations and reporting of model fairness are required to build the evidence base for AI-based tool design for cancer and to ensure that these emerging technologies promote equitable healthcare.

Short-chain L-3-hydroxyacyl-CoA dehydrogenase: A novel vital oncogene or tumor suppressor gene in cancers

The reprogramming of cellular metabolism is frequently linked to tumorigenesis. Glucose, fatty acids, and amino acids are the specific substrates involved in how an organism maintains metabolic equilibrium. The HADH gene codes for the short-chain L-3-hydroxyacyl-CoA dehydrogenase (HADH), a crucial enzyme in fatty acid oxidation that catalyzes the third phase of fatty acid oxidation in mitochondria. Increasing data suggest that HADH is differentially expressed in various types of malignancies and is linked to cancer development and progression. The significance of HADH expression in tumors and its potential mechanisms of action in the onset and progression of certain cancers are summarized in this article. The possible roles of HADH as a target and/or biomarker for the detection and treatment of various malignancies is also described here.

Effects of physical exercise on body fat and laboratory biomarkers in cancer patients: a meta-analysis of 35 randomized controlled trials

BACKGROUND

A growing number of articles had reported the beneficial effects of physical exercise on reduced risks of cancer recurrence and mortality. However, the associations between physical exercise and laboratory biomarkers still had controversy. As we knew, this meta-analysis of randomized controlled trials (RCTs) was the first time for us to comprehensively clarify their relationships in cancer patients.

METHODS

We comprehensively searched the PubMed, Cochrane Central, EMBASE, Web of Science, and SportDiscus online databases to identify eligible articles, up to June 1, 2021. Pooled standardized mean differences (SMDs) with 95% confidence intervals (CIs) were utilized to clarify their associations. Sensitivity analysis was performed to assess the impact of the individual on overall and Begg's/Egger's plot was utilized to evaluate potential publication bias.

RESULTS

Finally, 35 randomized controlled trials (RCTs) were finally enrolled in this meta-analysis. Our results indicated that physical exercise could significantly reduce BMI (pooled SMD = -0.32 - 0.56 to -0.09)), body weight (pooled SMD = -0.31 (-0.54 to -0.08)), body fat (pooled SMD = -0.44 (-0.70 to -0.18)), waist circumference (pooled SMD = -0.50 (-0.76 to -0.23)), hip circumference (pooled SMD = -0.54 (-0.80 to -0.28)), triglyceride (pooled SMD = -0.35 (-0.69 to -0.02)), fasting insulin (pooled SMD = -0.38 (-0.54 to -0.22)), glucose (pooled SMD = -0.56 (-0.84 to -0.28)), insulin resistance (pooled SMD = -0.40 (-0.72 to -0.07)), CRP (pooled SMD = -0.97 (-1.48 to -0.46)), IGF-1 levels (pooled SMD = -0.56 (-0.83 to -0.29)) and remarkably increase IGFBP-3 levels (pooled SMD = 0.81 (0.45 to 1.17)). Further sensitivity analysis and Begg's or Egger's test suggested that our results were robust with no significant publication bias.

CONCLUSIONS

Our results shed light on the beneficial effects of physical exercise on cancer patients by means of BMI/weight change and various biomarkers alteration (insulin-glucose pathways or inflammatory biomarkers). Our results were anticipated for clinical application to improve cancer patients' prognosis.

TC2N: A Novel Vital Oncogene or Tumor Suppressor Gene In Cancers

Several C2 domain-containing proteins play key roles in tumorigenesis, signal transduction, and mediating protein–protein interactions. Tandem C2 domains nuclear protein (TC2N) is a tandem C2 domain-containing protein that is differentially expressed in several types of cancers and is closely associated with tumorigenesis and tumor progression. Notably, TC2N has been identified as an oncogene in lung and gastric cancer but as a tumor suppressor gene in breast cancer. Recently, a large number of tumor-associated antigens (TAAs), such as heat shock proteins, alpha-fetoprotein, and carcinoembryonic antigen, have been identified in a variety of malignant tumors. Differences in the expression levels of TAAs between cancer cells and normal cells have led to these antigens being investigated as diagnostic and prognostic biomarkers and as novel targets in cancer treatment. In this review, we summarize the clinical characteristics of TC2N-positive cancers and potential mechanisms of action of TC2N in the occurrence and development of specific cancers. This article provides an exploration of TC2N as a potential target for the diagnosis and treatment of different types of cancers.